Module 12 Animated and Interactive Graphics
Interactive and animated graphics are one of the major advantages of using the Rmarkdown ecosystem - because you can easily create web pages in markdown (without the pain of HTML), you aren’t limited by paper any more. We’ll cover two different technologies that allow you to create different types of interactive charts, graphs, and interfaces.
It is helpful to think about interactivity in a couple of different ways:
What does it require? Do you need to be doing statistical calculations in the background, or can you precompute all of the data ahead of time?
What type of activity or interactivity do you need?
- Zoom in/out?
- Provide additional information in response to user actions (mouseover, click)
- Provide information over time (animation)
- Keep track of a data point over multiple plots? (linked plots)
- Keep track of one or more data points and change their appearance based on user interaction (brushing)
- Allow the user to change the underlying statistical model or data?
(This is not a full list of all of the types of interactivity, just a few of the more common options)
In this section, we’ll cover two ways to easily create interactive graphics or applets in R. There are, of course, many others – many javascript libraries have R extensions of one form or another.
Module Objectives
- Create animated and interactive charts using appropriate tools
12.1 Plotly
Plotly is a graphing library that uses javascript to add interactivity to graphics. There are several different ways to create plotly graphs in R, but by far the easiest is ggplotly, which converts a ggplot to a plotly plot automatically (so you don’t have to specify most of the details).
12.1.1 ggplotly: ggplot2 to plotly conversions
Set up the data
if (!"plotly" %in% installed.packages()) install.packages("plotly")
if (!"tidytuesdayR" %in% installed.packages()) {
devtools::install_github("thebioengineer/tidytuesdayR")
}
library(dplyr)
library(tidyr)
library(ggplot2)
library(tibble)
library(lubridate) # dates and times
library(tidytuesdayR) # get interesting data
library(plotly)
library(stringr)
# Load the data from TidyTuesday on May 12
full_data <- tt_load('2020-05-12')
volcano <- full_data$volcano
eruptions <- full_data$eruptions
events <- full_data$events
sulfur <- full_data$sulfur
trees <- full_data$tree_ringsLet’s try out plotly while doing a bit of exploratory data analysis on this dataset.
Cleaning up volcano
volcano <- volcano %>%
filter(tectonic_settings != "Unknown") %>%
separate(tectonic_settings, into = c("zone", "crust"), sep = "/", remove = F) %>%
# Remove anything past the first punctuation character - that will catch (xx) and ?
mutate(volcano_type = str_remove(primary_volcano_type, "[[:punct:]].*$"))Let’s start by seeing whether the elevation of a volcano changes based on the type of zone it’s on - we might expect that Rift zone volcanos (where plates are pulling away from each other) might not be as high.
p <- volcano %>%
ggplot(aes(x = zone, y = elevation)) +
geom_boxplot() +
coord_flip()
ggplotly(p)Does volcano type makes a difference?
p <- volcano %>%
ggplot(aes(x = elevation, color = volcano_type)) +
geom_density() +
# Rug plots show each observation as a tick just below the x axis
geom_rug()
ggplotly(p)Here, the interactivity actually helps a bit: we don’t need to use the legend to see what each curve corresponds to. We can see that submarine volcanoes are typically much lower in elevation (ok, duh), but also that subglacial volcanoes are found in a very limited range. If we double-click on a legend entry, we can get rid of all other curves and examine each curve one by one.
I added the rug layer after the initial bout because I was curious how much data each of these curves were based on. If we want only curves with n > 10 observations, we can do that:
p <- volcano %>%
group_by(volcano_type) %>% mutate(n = n()) %>%
filter(n > 10) %>%
ggplot(aes(x = elevation, color = volcano_type)) +
geom_density() +
# Rug plots show each observation as a tick just below the x axis
geom_rug(aes(text = paste0(volcano_name, ", ", country)))
Warning: Ignoring unknown aesthetics: text
ggplotly(p)If we want to specify additional information that should show up in the tooltip, we can do that as well by adding the text aesthetic even though geom_rug doesn’t take a text aesthetic. You may notice that ggplot2 complains about the unknown aesthetic I’ve added to geom_rug: That allows us to mouse over each data point in the rug plot and see what volcano it belongs to. So we can tell from the rug plot that the tallest volcano is Ojas de Salvado, in Chile/Argentina (I believe that translates to Eyes of Salvation?).
ggplotly makes it very easy to generate plots that have a ggplot2 equivalent; you can customize these plots further using plotly functions that we’ll see in the next section. But first, try the interface out on your own.
Try it out
Conduct an exploratory data analysis of the eruptions dataset. What do you find?
My solution
head(eruptions)
# A tibble: 6 x 15
volcano_number volcano_name eruption_number eruption_catego… area_of_activity
<dbl> <chr> <dbl> <chr> <chr>
1 266030 Soputan 22354 Confirmed Erupt… <NA>
2 343100 San Miguel 22355 Confirmed Erupt… <NA>
3 233020 Fournaise, P… 22343 Confirmed Erupt… <NA>
4 345020 Rincon de la… 22346 Confirmed Erupt… <NA>
5 353010 Fernandina 22347 Confirmed Erupt… <NA>
6 273070 Taal 22344 Confirmed Erupt… <NA>
# … with 10 more variables: vei <dbl>, start_year <dbl>, start_month <dbl>,
# start_day <dbl>, evidence_method_dating <chr>, end_year <dbl>,
# end_month <dbl>, end_day <dbl>, latitude <dbl>, longitude <dbl>
summary(eruptions %>% mutate(eruption_category = factor(eruption_category)))
volcano_number volcano_name eruption_number
Min. :210010 Length:11178 Min. :10001
1st Qu.:263310 Class :character 1st Qu.:12817
Median :290050 Mode :character Median :15650
Mean :300284 Mean :15667
3rd Qu.:343030 3rd Qu.:18464
Max. :600000 Max. :22355
eruption_category area_of_activity vei
Confirmed Eruption :9900 Length:11178 Min. :0.000
Discredited Eruption: 166 Class :character 1st Qu.:1.000
Uncertain Eruption :1112 Mode :character Median :2.000
Mean :1.948
3rd Qu.:2.000
Max. :7.000
NA's :2906
start_year start_month start_day evidence_method_dating
Min. :-11345.0 Min. : 0.000 Min. : 0.000 Length:11178
1st Qu.: 680.0 1st Qu.: 0.000 1st Qu.: 0.000 Class :character
Median : 1847.0 Median : 1.000 Median : 0.000 Mode :character
Mean : 622.8 Mean : 3.451 Mean : 7.015
3rd Qu.: 1950.0 3rd Qu.: 7.000 3rd Qu.:15.000
Max. : 2020.0 Max. :12.000 Max. :31.000
NA's :1 NA's :193 NA's :196
end_year end_month end_day latitude
Min. :-475 Min. : 0.000 Min. : 0.00 Min. :-77.530
1st Qu.:1895 1st Qu.: 3.000 1st Qu.: 4.00 1st Qu.: -6.102
Median :1957 Median : 6.000 Median :15.00 Median : 17.600
Mean :1917 Mean : 6.221 Mean :13.32 Mean : 16.866
3rd Qu.:1992 3rd Qu.: 9.000 3rd Qu.:21.00 3rd Qu.: 40.821
Max. :2020 Max. :12.000 Max. :31.00 Max. : 85.608
NA's :6846 NA's :6849 NA's :6852
longitude
Min. :-179.97
1st Qu.: -77.66
Median : 55.71
Mean : 31.57
3rd Qu.: 139.39
Max. : 179.58
# Historical (very historical) dates are a bit of a pain to work with, so I
# wrote a helper function which takes year, month, and day arguments and formats
# them properly
fix_date <- function(yyyy, mm, dd) {
# First, negative years (BCE) are a bit of a problem.
neg <- yyyy < 0
subtract_years <- pmax(-yyyy, 0) # Years to subtract off later
# for now, set to 0
year_fixed <- pmax(yyyy, 0) # this will set anything negative to 0
# sometimes the day or month isn't known, so just use 1 for both.
# recorded value may be NA or 0.
day_fixed <- ifelse(is.na(dd), 1, pmax(dd, 1))
month_fixed <- ifelse(is.na(mm), 1, pmax(mm, 1))
# Need to format things precisely, so use sprintf
# %0xd ensures that you have at least x digits, padding the left side with 0s
# lubridate doesn't love having 3-digit years.
date_str <- sprintf("%04d/%02d/%02d", year_fixed, month_fixed, day_fixed)
# Then we can convert the dates and subtract off the years for pre-CE dates
date <- ymd(date_str) - years(subtract_years)
}
erupt <- eruptions %>%
# Don't work with discredited eruptions
filter(eruption_category == "Confirmed Eruption") %>%
# Create start and end dates
mutate(
start_date = fix_date(start_year, start_month, start_day),
end_date = fix_date(end_year, end_month, end_day),
# To get duration, we have to start with a time interval,
# convert to duration, then convert to a numeric value
duration = interval(start = start_date, end = end_date) %>%
as.duration() %>%
as.numeric("days"))
Warning: 1 failed to parse.
Warning: 5895 failed to parse.Let’s start out seeing what month most eruptions occur in…
# Note, I'm using the original month, so 0 = unknown
p <- ggplot(erupt, aes(x = factor(start_month))) + geom_bar()
ggplotly(p)Another numerical variable is VEI, volcano explosivity index. A VEI of 0 is non-explosive, a VEI of 4 is about what Mt. St. Helens hit in 1980, and a VEI of 5 is equivalent to the Krakatau explosion in 1883. A VEI of 8 would correspond to a major Yellowstone caldera eruption (which hasn’t happened for 600,000 years). Basically, VEI increase of 1 is an order of magnitude change in the amount of material the eruption released.
# VEI is volcano explosivity index,
p <- ggplot(erupt, aes(x = vei)) + geom_bar()
ggplotly(p)
Warning: Removed 2270 rows containing non-finite values (stat_count).We can also look at the frequency of eruptions over time. We’ll expect some historical bias - we don’t have exact dates for some of these eruptions, and if no one was around to write the eruption down (or the records were destroyed) there’s not going to be a date listed here.
p <- erupt %>%
filter(!is.na(end_date)) %>%
filter(start_year > 0) %>%
ggplot(aes(x = start_date, xend = start_date,
y = 0, yend = duration,
color = evidence_method_dating)) +
geom_segment() +
geom_point(size = .5, aes(text = volcano_name)) +
xlab("Eruption Start") +
ylab("Eruption Duration (days)") +
facet_wrap(~vei, scales = "free_y")
Warning: Ignoring unknown aesthetics: text
ggplotly(p)As expected, it’s pretty rare to see many eruptions before ~1800 AD, which is about when we have reliable historical records42 for most of the world (exceptions include e.g. Vestuvius, which we have extensive written information about).
p <- erupt %>%
filter(!is.na(end_date)) %>%
# Account for recency bias (sort of)
filter(start_year > 1800) %>%
ggplot(aes(x = factor(vei), y = duration)) +
geom_violin() +
xlab("VEI") +
ylab("Eruption Duration (days)") +
scale_y_sqrt()
ggplotly(p)
Warning in self$trans$transform(x): NaNs produced
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Removed 3 rows containing non-finite values (stat_ydensity).
Warning: Groups with fewer than two data points have been dropped.12.1.2 plot_ly: Like base plotting, but interactive!
You can also create plotly charts that aren’t limited by what you can do in ggplot2, using the plot_ly function.
We can start with a scatterplot of volcanoes along the Earth’s surface:
plot_ly(type = "scattergeo", lon = volcano$longitude, lat = volcano$latitude)
No scattergeo mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plotly.com/r/reference/#scatter-modeAnd then we can start customizing:
plot_ly(type = "scattergeo", lon = volcano$longitude, lat = volcano$latitude,
mode = "markers",
# Add information to mouseover
text = ~paste(volcano$volcano_name, "\n",
"Last Erupted: ", volcano$last_eruption_year),
# Change the markers because why not?
marker = list(color = "#d00000", opacity = 0.25)
)
The plot_ly function is also pipe friendly. Variable mappings are preceded with ~ to indicate that the visual appearance changes with the value of the variable.
# Load RColorBrewer for palettes
library(RColorBrewer)
volcano %>%
group_by(volcano_type) %>% mutate(n = n()) %>%
filter(n > 15) %>%
plot_ly(type = "scattergeo", lon = ~longitude, lat = ~latitude,
mode = "markers",
# Add information to mouseover
text = ~paste(volcano_name, "\n",
"Last Erupted: ", last_eruption_year),
color = ~ volcano_type,
# Specify a palette
colors = brewer.pal(length(unique(.$volcano_type)), "Paired"),
# Change the markers because why not?
marker = list(opacity = 0.5)
)The plotly documentation often uses plyr and reshape2 – which are older versions of dplyr and tidyr. If you load plyr and reshape2, it may seriously mess up your day – a lot of the function names are the same. So, instead, here’s a shortcut: cast is pivot_wider and melt is pivot_longer. That should at least help with understanding what the code is doing.
If you do accidentally load plyr or reshape2, that’s fine: just restart your R session so that your loaded packages are cleared and you can start over. Or, if you must, you can reference a plyr function using plyr::function_name without loading the package – that’s a safe way to use the plotly demo code as-is.
Let’s explore traces a bit.
According to the plotly documentation,
A trace is just the name we give a collection of data and the specifications of which we want that data plotted. Notice that a trace will also be an object itself, and these will be named according to how you want the data displayed on the plotting surface
In ggplot2 terms, it seems that a trace is somewhat akin to a geom.
trace0 <- rnorm(100, mean = 5)
trace1 <- rnorm(100, mean = 0)
trace2 <- rnorm(100, mean = -5)
data <- tibble(x = 1:100, trace0, trace1, trace2)
# Let's see how this goes with one trace
plot_ly(data, x = ~x) %>%
add_trace(y = ~trace0, name = 'trace0', mode = 'lines')
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plotly.com/r/reference/#scatterBut, if you want all of the variables to be shown with the same trace type, it’s probably easier to get to long form:
data %>%
pivot_longer(matches("trace"), names_to = "trace", names_prefix = "trace", values_to = "y") %>%
plot_ly(x = ~x, y = ~y, color = ~trace, mode = "lines+markers")
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plotly.com/r/reference/#scatterThere are many different trace types in plotly, but your best bet is to check the documentation to see what is available.
12.1.3 Animation
Plotly can also animate your plots for you.
library(classdata)
data(fbi)
fbi %>%
mutate(State = factor(State),
Rate_100k = Count/Population*100000) %>%
filter(Type == "Aggravated.assault") %>%
arrange(Year, State, Type) %>%
plot_ly(
x = ~State,
y = ~Rate_100k,
color = ~Type,
frame = ~Year,
type = "scatter",
mode = "markers"
) Sometimes the animations get a bit trippy, don’t they?
You can even animate by something other than time, if you’re so inclined, though it’s not necessarily going to make sense if there isn’t any context shared between successive observations. So animating over space might make sense, but animating over a factor makes a lot less sense.
fbi %>%
mutate(State = factor(State),
Rate_100k = Count/Population*100000) %>%
arrange(Year, State, Type) %>%
plot_ly(
x = ~Year,
y = ~Rate_100k,
color = ~Type,
frame = ~State,
type = "scatter",
mode = "lines"
) There are other types of animations as well, including the ability to change plot formats, trace types, and more.
12.2 Leaflet maps
Leaflet is another javascript library that allows for interactive data visualization. We’re only going to briefly talk about it here, but there is extensive documentation that includes details of how to work with different types of geographical data, chloropleth maps, plugins, and more.
To explore the leaflet package, we’ll start out playing with a dataset of Bigfoot sightings assembled from the Bigfoot Field Researchers Organization’s Google earth tool
── Column specification ────────────────────────────────────────────────────────
cols(
.default = col_double(),
observed = col_character(),
location_details = col_character(),
county = col_character(),
state = col_character(),
season = col_character(),
title = col_character(),
date = col_date(format = ""),
classification = col_character(),
geohash = col_character(),
precip_type = col_character(),
summary = col_character()
)
ℹ Use `spec()` for the full column specifications.if (!"leaflet" %in% installed.packages()) install.packages("leaflet")
library(leaflet)
library(readr)
bigfoot_data <- read_csv("https://query.data.world/s/egnaxxvegdkzzrhfhdh4izb6etmlms")We can start out by plotting a map with the location of each sighting. I’ve colored the points in a seasonal color scheme, and added the description of each incident as a mouseover label.
bigfoot_data %>%
filter(classification == "Class A") %>%
mutate(seasoncolor = str_replace_all(season, c("Fall" = "orange",
"Winter" = "skyblue",
"Spring" = "green",
"Summer" = "yellow")),
# This code just wraps the description to the width of the R terminal
# and inserts HTML for a line break into the text at appropriate points
desc_wrap = purrr::map(observed, ~strwrap(.) %>%
paste(collapse = "<br/>") %>%
htmltools::HTML())) %>%
leaflet() %>%
addTiles() %>%
addCircleMarkers(~longitude, ~latitude, color = ~seasoncolor, label = ~desc_wrap)
Warning in validateCoords(lng, lat, funcName): Data contains 459 rows with
either missing or invalid lat/lon values and will be ignoredOf course, because this is an interactive map library, we aren’t limited to any one scale. We can also plot data at the city level:
if(!"nycsquirrels18" %in% installed.packages()) {
devtools::install_github("mine-cetinkaya-rundel/nycsquirrels18")
}
library(nycsquirrels18)
data(squirrels)
head(squirrels)
# A tibble: 6 x 35
long lat unique_squirrel_… hectare shift date hectare_squirrel… age
<dbl> <dbl> <chr> <chr> <chr> <date> <dbl> <chr>
1 -74.0 40.8 13A-PM-1014-04 13A PM 2018-10-14 4 <NA>
2 -74.0 40.8 15F-PM-1010-06 15F PM 2018-10-10 6 Adult
3 -74.0 40.8 19C-PM-1018-02 19C PM 2018-10-18 2 Adult
4 -74.0 40.8 21B-AM-1019-04 21B AM 2018-10-19 4 <NA>
5 -74.0 40.8 23A-AM-1018-02 23A AM 2018-10-18 2 Juve…
6 -74.0 40.8 38H-PM-1012-01 38H PM 2018-10-12 1 Adult
# … with 27 more variables: primary_fur_color <chr>, highlight_fur_color <chr>,
# combination_of_primary_and_highlight_color <chr>, color_notes <chr>,
# location <chr>, above_ground_sighter_measurement <chr>,
# specific_location <chr>, running <lgl>, chasing <lgl>, climbing <lgl>,
# eating <lgl>, foraging <lgl>, other_activities <chr>, kuks <lgl>,
# quaas <lgl>, moans <lgl>, tail_flags <lgl>, tail_twitches <lgl>,
# approaches <lgl>, indifferent <lgl>, runs_from <lgl>,
# other_interactions <chr>, zip_codes <dbl>, community_districts <dbl>,
# borough_boundaries <dbl>, city_council_districts <dbl>,
# police_precincts <dbl>
squirrels %>%
mutate(color = tolower(primary_fur_color)) %>%
leaflet() %>%
addTiles() %>%
addCircleMarkers(~long, ~lat, color = ~color)We can also plot regions, instead of just points. I downloaded a dataset released by the US Forest Service, Bailey’s Ecoregions and Subregions dataset, which categorizes the US into different climate and ecological zones.
To map colors to variables, we have to define a color palette and variable mapping ourselves, and pass that function into the leaflet object we’re adding.
library(rgdal)
Loading required package: sp
rgdal: version: 1.5-23, (SVN revision 1121)
Geospatial Data Abstraction Library extensions to R successfully loaded
Loaded GDAL runtime: GDAL 3.0.4, released 2020/01/28
Path to GDAL shared files: /usr/share/gdal
GDAL binary built with GEOS: TRUE
Loaded PROJ runtime: Rel. 6.3.1, February 10th, 2020, [PJ_VERSION: 631]
Path to PROJ shared files: /usr/share/proj
Linking to sp version:1.4-5
To mute warnings of possible GDAL/OSR exportToProj4() degradation,
use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.
ecoregions <- readOGR("data/Bailey_s_Ecoregions_and_Subregions_Dataset.geojson")
OGR data source with driver: GeoJSON
Source: "/home/susan/Projects/Class/unl-stat850/stat850-textbook/data/Bailey_s_Ecoregions_and_Subregions_Dataset.geojson", layer: "Bailey_s_Ecoregions_and_Subregions_Dataset"
with 3072 features
It has 12 fields
# Define a palette
region_pal <- colorFactor(c("#E67E22", "#0B5345", "#229954", "#B3B6B7"), ecoregions$DOMAIN)
ecoregions %>%
leaflet() %>%
addTiles() %>%
addPolygons(stroke = TRUE, fillOpacity = 0.25,
fillColor = ~region_pal(DOMAIN), color = ~region_pal(DOMAIN), label = ~SECTION)